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<h2>Documentation for GPML Matlab Code</h2>

The code provided here demonstrates the main algorithms from Rasmussen
and Williams: <a href="http://www.gaussianprocess.org/gpml">Gaussian Processes
for Machine Learning</a>.</p>

The code is written in Matlab&reg, and should work with version 6 and
version 7.  Bug reports should be sent to the authors. All the code
including demonstrations and html documentation can be downloaded in a
<a
href="http://www.gaussianprocess.org/gpml/code/gpml-matlab.tar.gz">tar</a>
or <a
href="http://www.gaussianprocess.org/gpml/code/gpml-matlab.zip">zip</a>
archive file. Previous versions of the code may be available <a
href="http://www.gaussianprocess.org/gpml/code/old">here</a>. Please
read the <a href="../gpml/Copyright">copyright</a> notice.</p>

After unpacking the tar or zip file you will find 
3 subdirectories: gpml, gpml-demo and doc.
</p>

The directory gpml contains the basic functions for GP regression,
GP binary classification, and sparse approximate methods for GP regression.
</p>

The directory gpml-demo contains 
Matlab&reg scripts with names "demo_*.m". These provide small
demonstrations of the various programs provided. 
</p>

The directory doc contains four html files providing 
documentation. This information can also be accessed via
the www at <a href="http://www.GaussianProcess.org/gpml/code">http://www.GaussianProcess.org/gpml/code</a>.
</p>

The code should run directly as provided, but some demos require a lot of
computation. A significant speedup may be attained by compiling the mex
files, see the rudimentary instructions on how to do this in the <a
href="../README">README</a> file.</p>

The documentation is divided into three sections:

<h3>Regression</h3>

Basic <a href="regression.html">Gaussian process regression</a> (GPR)
code allowing flexible specification of the covariance function.

<h3>Binary Classification</h3>

<a href="classification.html">Gaussian process classification</a> (GPC)
demonstrates implementations of Laplace and EP approximation methods for binary
GP classification.

<h3>Sparse Approximation methods for Gaussian Process Regression</h3>

<a href="sparse-approx.html">Approximation methods for GPR</a> demonstrates the
methods of <b>subset of datapoints</b> (SD), <b>subset of regressors</b> (SR)
and <b>projected process</b> (PP) approximations.

<br><br><br>

<h3>Other Gaussian Process Code</h3>

A table of other sources of useful Gaussian process software, unrelated to the
<a href="http://www.gaussianprocess.org/gpml">book</a>, may be found <a
href="http://www.gaussianprocess.org/#code">here<a>. This 
includes pointers a number of packages that can handle multi-class
classification, e.g. <tt>fbm</tt> (Radford Neal), 
<tt>c++-ivm</tt> (Neil Lawrence), <tt>gpclass</tt> (David
Barber and Chris Williams), <tt>klr</tt> (kernel multiple
logistic regression, by Matthias Seeger), and 
<tt>VBGP</tt> (Mark Girolami and Simon Rogers).
</p>

<br><br><br>

Go back to the <a href="http://www.gaussianprocess.org/gpml">web page</a> for
Gaussian Processes for Machine Learning.

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